08.小提琴图绘制
清除当前环境中的变量
rm(list=ls())
设置工作目录
setwd("C:/Users/Dell/Desktop/R_Plots/08vioplot/")
vioplot包绘制小提琴图
library(vioplot)
# formula input
# 加载示例数据iris
data("iris")
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
vioplot(Sepal.Length~Species, data = iris,
main = "Sepal Length", # 设置标题
col=c("lightgreen", "lightblue", "palevioletred")) # 设置小提琴颜色
# 添加图例
legend("topleft", legend=c("setosa", "versicolor", "virginica"),
fill=c("lightgreen", "lightblue", "palevioletred"), cex = 1.2)
image.png
# 加载示例数据
data("diamonds", package = "ggplot2")
head(diamonds)
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# 设置画板颜色
palette <- RColorBrewer::brewer.pal(9, "Pastel1")
palette
## [1] "#FBB4AE" "#B3CDE3" "#CCEBC5" "#DECBE4" "#FED9A6" "#FFFFCC" "#E5D8BD"
## [8] "#FDDAEC" "#F2F2F2"
par(mfrow=c(3, 1))
vioplot(price ~ cut, data = diamonds, las = 1, col = palette)
vioplot(price ~ clarity, data = diamonds, las = 2, col = palette)
vioplot(price ~ color, data = diamonds, las = 2, col = palette)
image.png
#generate example data
data_one <- rnorm(100)
data_two <- rnorm(50, 1, 2)
head(data_one)
## [1] -0.8567350 0.2851433 -0.1948110 -0.7102499 -1.0780155 0.2462039
head(data_two)
## [1] 2.164083 3.144620 1.190473 2.285546 3.494566 1.492088
par(mfrow=c(2,2))
#colours can be customised separately, with axis labels, legends, and titles
vioplot(data_one, data_two,
col=c("red","blue"), #设置小提琴颜色
names=c("data one", "data two"),
main="data violin",
xlab="data class", ylab="data read")
legend("topleft", fill=c("red","blue"), legend=c("data one", "data two"))
#colours can be customised for the violin fill and border separately
vioplot(data_one, data_two,
col="grey85", border="purple",
names=c("data one", "data two"),
main="data violin",
xlab="data class", ylab="data read")
#colours can also be customised for the boxplot rectange and lines (border and whiskers)
vioplot(data_one, data_two,
col="grey85", rectCol="lightblue", lineCol="blue",
border="purple", names=c("data one", "data two"),
main="data violin", xlab="data class", ylab="data read")
#these colours can also be customised separately for each violin
vioplot(data_one, data_two,
col=c("skyblue", "plum"),
rectCol=c("lightblue", "palevioletred"),
lineCol="blue", border=c("royalblue", "purple"),
names=c("data one", "data two"),
main="data violin", xlab="data class", ylab="data read")
image.png
par(mfrow=c(1,1))
#this applies to any number of violins, given that colours are provided for each
vioplot(data_one, data_two, rnorm(200, 3, 0.5), rpois(200, 2.5), rbinom(100, 10, 0.4),
col=c("red", "orange", "green", "blue", "violet"), horizontal = T,
rectCol=c("palevioletred", "peachpuff", "lightgreen", "lightblue", "plum"),
lineCol=c("red4", "orangered", "forestgreen", "royalblue", "mediumorchid"),
border=c("red4", "orangered", "forestgreen", "royalblue", "mediumorchid"),
names=c("data one", "data two", "data three", "data four", "data five"),
main="data violin", xlab="data class", ylab="data read")
image.png
ggplot2包绘制小提琴图
library(ggplot2)
# 查看示例数据
head(diamonds)
## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
# Basic plot
ggplot(diamonds,aes(cut,log(price),fill=cut)) +
geom_violin()
image.png
# 更换填充色，设置分面
ggplot(diamonds,aes(cut,log(price),fill=cut)) +
geom_violin() +
scale_fill_manual(values = c("palevioletred", "peachpuff", "lightgreen", "lightblue", "plum")) +
facet_wrap(.~clarity,ncol = 4)
image.png
# 添加箱线图和均值点
ggplot(diamonds,aes(cut,log(price),fill=cut)) +
geom_violin() +
geom_boxplot(width=0.1,position = position_identity(),fill="white") +
stat_summary(fun.y="mean",geom="point",shape=23, size=4,fill="red") +
theme_bw() + theme(legend.position = "top")
image.png
ggpubr包绘制小提琴图
library(ggpubr)
# 加载示例数据
data("ToothGrowth")
df <- ToothGrowth
head(df)
## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
# Basic plot
ggviolin(df, x = "dose", y = "len", color = "supp")
image.png
# Change the plot orientation: horizontal
ggviolin(df, "dose", "len", fill = "supp",orientation = "horiz")
image.png
# Add box plot
ggviolin(df, x = "dose", y = "len", fill = "dose",
add = "boxplot",add.params = list(fill="white"))
image.png
ggviolin(df, x = "dose", y = "len", fill = "supp",
add = "dotplot")
image.png
# Add jitter points and
# change point shape by groups ("dose")
ggviolin(df, x = "dose", y = "len", fill = "supp",
add = "jitter", shape = "dose")
image.png
# Add mean_sd + jittered points
ggviolin(df, x = "dose", y = "len", fill = "dose",
add = c("jitter", "mean_sd"))
image.png
# Change error.plot to "crossbar"
ggviolin(df, x = "dose", y = "len", fill = "dose",
add = "mean_sd", error.plot = "crossbar")
image.png
# Change colors
# Change outline colors by groups: dose
# Use custom color palette and add boxplot
ggviolin(df, "dose", "len", color = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
add = "boxplot")
image.png
# Change fill color by groups: dose
# add boxplot with white fill color
ggviolin(df, "dose", "len", fill = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
add = "boxplot", add.params = list(fill = "white"))
image.png
ggviolin(df, "dose", "len", facet.by = "supp", color = "supp",
palette = c("#00AFBB", "#E7B800"), add = "boxplot")
image.png
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936
## [2] LC_CTYPE=Chinese (Simplified)_China.936
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C
## [5] LC_TIME=Chinese (Simplified)_China.936
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.2.1 magrittr_1.5 ggplot2_3.2.0 vioplot_0.3.2 zoo_1.8-6
## [6] sm_2.2-5.6
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 pillar_1.4.2 compiler_3.6.0
## [4] RColorBrewer_1.1-2 tools_3.6.0 zeallot_0.1.0
## [7] digest_0.6.20 viridisLite_0.3.0 evaluate_0.14
## [10] tibble_2.1.3 gtable_0.3.0 lattice_0.20-38
## [13] pkgconfig_2.0.2 rlang_0.4.0 cli_1.1.0
## [16] yaml_2.2.0 xfun_0.8 withr_2.1.2
## [19] stringr_1.4.0 dplyr_0.8.3 knitr_1.23
## [22] vctrs_0.2.0 grid_3.6.0 tidyselect_0.2.5
## [25] glue_1.3.1 R6_2.4.0 fansi_0.4.0
## [28] tcltk_3.6.0 rmarkdown_1.13 purrr_0.3.2
## [31] backports_1.1.4 scales_1.0.0 htmltools_0.3.6
## [34] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.5.0
## [37] labeling_0.3 utf8_1.1.4 stringi_1.4.3
## [40] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4
